import os import cv2 import torch import torch.nn as nn import torch.nn.functional as F import torchvision.transforms as transforms from tqdm import tqdm from PIL import Image from dreamsim import dreamsim from decord import VideoReader from torchvision.io import write_video from vbench.subject_consistency import compute_subject_consistency, subject_consistency from vbench.utils import load_video, load_dimension_info, dino_transform, dino_transform_Image from vbench2_beta_long.utils import reorganize_clips_results, create_video_from_first_frames, fuse_inclip_clip2clip from vbench2_beta_long.utils import dreamsim_transform, dreamsim_transform_Image, dinov2_transform, dinov2_transform_Image import logging logging.basicConfig(level = logging.INFO,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) def compute_long_subject_consistency(json_dir, device, submodules_list, **kwargs): # compute inclip scores all_results, detailed_results = compute_subject_consistency(json_dir, device, submodules_list) inclip_all_results, inclip_detailed_results, inclip_average_scores = reorganize_clips_results(detailed_results) inclip_all_results = all_results # compute clip2clip scores # sample first frames in each clip, and cat them into a new video base_path_video = os.path.dirname(list(detailed_results[0].values())[0]).split("split_clip")[0] long_video_path = os.path.join(base_path_video, "split_clip") new_cat_video_path = os.path.join(base_path_video, 'subject_consistency_cat_firstframes_videos') if not os.path.exists(new_cat_video_path): os.makedirs(new_cat_video_path, exist_ok=True) create_video_from_first_frames(long_video_path, new_cat_video_path, detailed_results) else: print(f"{new_cat_video_path} has already been created, please check the path") # get the new video_list video_list = [] for video_path in os.listdir(new_cat_video_path): video_list.append(os.path.join(new_cat_video_path, video_path)) def _compute_subject_consistency(video_list, device, submodules_list, **kwargs): if kwargs['sb_clip2clip_feat_extractor'] == 'dino': dino_model = torch.hub.load(**submodules_list).to(device) read_frame = submodules_list['read_frame'] logger.info("Initialize DINO success") all_results, video_results = subject_consistency(dino_model, video_list, device, read_frame) elif kwargs['sb_clip2clip_feat_extractor'] == 'dinov2': dinov2_dict = { 'repo_or_dir': f'facebookresearch/dinov2', 'model': 'dinov2_vitb14', } dinov2_model = torch.hub.load(**dinov2_dict).to(device) read_frame = submodules_list['read_frame'] logger.info("Initialize DINOv2 success") all_results, video_results = subject_consistency_dinov2(dinov2_model, video_list, device, read_frame) elif kwargs['sb_clip2clip_feat_extractor'] == 'dreamsim': read_frame = submodules_list['read_frame'] cache_dir = os.path.expanduser("~/.cache") dreamsim_model, _ = dreamsim(pretrained=True, cache_dir=cache_dir) all_results, video_results = subject_consistency_dreamsim(dreamsim_model, video_list, device, read_frame) return all_results, video_results clip2clip_all_results, clip2clip_detailed_results = _compute_subject_consistency(video_list, device, submodules_list, **kwargs) dimension = 'subject_consistency' fused_all_results, fused_detailed_results = fuse_inclip_clip2clip(inclip_all_results, clip2clip_all_results, inclip_average_scores, clip2clip_detailed_results, dimension, **kwargs) # fused_all_results = inclip_all_results * kwargs['w_inclip'] + clip2clip_all_results * kwargs['w_clip2clip'] return fused_all_results, fused_detailed_results def subject_consistency_dreamsim(model, video_list, device, read_frame): sim = 0.0 cnt = 0 video_results = [] if read_frame: image_transform = dreamsim_transform_Image(224) else: image_transform = dreamsim_transform(224) # image_transform = preprocess if read_frame else dino_transform(224) for video_path in tqdm(video_list): video_sim = 0.0 if read_frame: video_path = video_path[:-4].replace('videos', 'frames').replace(' ', '_') tmp_paths = [os.path.join(video_path, f) for f in sorted(os.listdir(video_path))] images = [] for tmp_path in tmp_paths: images.append(image_transform(Image.open(tmp_path))) images = torch.stack(images) else: images = load_video(video_path) images = image_transform(images) images = images.to(device) for i in range(len(images)): with torch.no_grad(): image = images[i].unsqueeze(0) image_features = model.embed(image) image_features = F.normalize(image_features, dim=-1, p=2) if i == 0: first_image_features = image_features else: sim_pre = max(0.0, F.cosine_similarity(former_image_features, image_features).item()) sim_fir = max(0.0, F.cosine_similarity(first_image_features, image_features).item()) cur_sim = (sim_pre + sim_fir) / 2 video_sim += cur_sim cnt += 1 former_image_features = image_features sim_per_images = video_sim / (len(images) - 1) sim += video_sim video_results.append({'video_path': video_path, 'video_results': sim_per_images}) # sim_per_video = sim / (len(video_list) - 1) sim_per_frame = sim / cnt if cnt != 0 else None return sim_per_frame, video_results def subject_consistency_dinov2(model, video_list, device, read_frame): sim = 0.0 cnt = 0 video_results = [] if read_frame: image_transform = dinov2_transform_Image(224) else: image_transform = dinov2_transform(224) for video_path in tqdm(video_list): video_sim = 0.0 if read_frame: video_path = video_path[:-4].replace('videos', 'frames').replace(' ', '_') tmp_paths = [os.path.join(video_path, f) for f in sorted(os.listdir(video_path))] images = [] for tmp_path in tmp_paths: images.append(image_transform(Image.open(tmp_path))) else: images = load_video(video_path) images = image_transform(images) for i in range(len(images)): with torch.no_grad(): image = images[i].unsqueeze(0) image = image.to(device) image_features = model(image) image_features = F.normalize(image_features, dim=-1, p=2) if i == 0: first_image_features = image_features else: sim_pre = max(0.0, F.cosine_similarity(former_image_features, image_features).item()) sim_fir = max(0.0, F.cosine_similarity(first_image_features, image_features).item()) cur_sim = (sim_pre + sim_fir) / 2 video_sim += cur_sim cnt += 1 former_image_features = image_features sim_per_images = video_sim / (len(images) - 1) sim += video_sim video_results.append({'video_path': video_path, 'video_results': sim_per_images}) # sim_per_video = sim / (len(video_list) - 1) sim_per_frame = sim / cnt if cnt != 0 else None return sim_per_frame, video_results